The Gist
- Agentic AI strengthens contact center workflows. As the hub for customer interactions, a contact center can integrate agentic AI to enhance data management, media handling, and support systems.
- Growing interest from CX leaders. Managers are actively exploring agentic AI for improving customer experience functions within contact centers.
- Pilot programs unlock potential. Launching a controlled pilot is the most effective way to test and measure agentic AI’s capabilities before a full-scale deployment.
Agentic AI is all the rage these days, with organizations exploring how AI can be infused into daily operations. The meaningful excitement lies in the application of agentic AI within a specific system.
One area of development that is seeing high interest is contact centers. Contact centers have been front and center in the customer experience leader’s arsenal to manage customer attention. Contact centers manage brand interactions across multiple channels, including phone, email, chat, social media and SMS.
Increased digital touchpoints have made contact centers the perfect foundation for agentic AI, network systems managed by a central large language model (LLM) controlling how network elements work together.
In this article, we will reveal what the introduction of AI in contact centers means for customer experience leaders looking to streamline their customer engagement.
Table of Contents
What Is a Contact Center?
To understand the value agentic AI brings to the contact center, let’s start with understanding the basics of what a contact center is.
A contact center is a platform designed to manage customer interactions, including phone calls, emails, chats, and social media. The center acts as a centralized repository that services various customer-related documents and associated media.
Contact centers are a more comprehensive approach to customer service than a traditional call center. A call center primarily focuses on voice calls, while contact centers incorporate the aforementioned channels as sources of customer touchpoints. Agents typically handle essential customer activities through these channels, such as customer support response calls, chats through mobile phone and confirmation emails.
A contact center ensures efficient and streamlined engagement processes that enhance how a brand connects with customers. It efficiently oversees customer communication and guides service agents to resolve customer inquiries and issues. For example, a solid contact center can minimize misrouted customer calls and boost overall service quality.
Related Article: What Is a Contact Center? Omnichannel Customer Experience Redefined
What Is Agentic AI in Contact Centers?
Agentic AI refers to AI systems that can act autonomously to complete tasks and make decisions without constant human oversight. In contact centers, this technology represents a shift from traditional chatbots that follow scripted responses to AI agents that can understand context, reason through problems and take independent actions to resolve customer issues.
Key applications of agentic AI in contact centers include:
- Autonomous customer service agents that handle complex inquiries end-to-end
- Intelligent call routing based on customer intent and agent capabilities
- Real-time coaching and assistance for human agents during interactions
- Automated resolution of routine tasks like account updates and order processing
- Predictive issue identification and proactive customer outreach
Business Impact Considerations of Agentic AI in Contact Centers
The technology aims to reduce operational costs while improving customer experience through faster resolution times and 24/7 availability. However, implementation requires careful consideration of customer acceptance, agent workforce implications and integration complexity with existing contact center infrastructure.
Where Does Agentic AI Assist Customer Service in the Contact Center?
Agentic AI is basically about building systems around large language models so the models can act with other devices using accurate, real-time data. They are AI-based systems designed to operate autonomously, accounting for their environment when executing actions, and learn from the results, all with minimal human intervention. It represents a shift from reactive AI, which responds to user prompts, to proactive, goal-oriented systems that can tackle complex tasks independently.
Why Agentic AI Fits Seamlessly into Contact Center Workflows
Agentic AI works well with contact centers because they are designed to manage the communication of an LLM with another device that customer experience leaders are relying on to deliver a customer experience. Agentic AI establishes activity patterns that allow LLMs to reflect more when responding to user prompts through devices or even other agents.
The result is fewer instances of brittle, hit-or-miss prompts that do not deliver what is needed. Organizations instead see instances where applying natural language leads to actually getting meaningful tasks important to the customer done.
What Is the Difference Between Generative AI and Agentic AI in Customer Service?
For example, an agentic AI within a contact center would leverage the various contacts to accurately answer a chain of density-style prompt. This arrangement differs from the sources a stand-alone generative AI solution relies upon to produce an answer.
Generative AI relies on its trained data, and with the latest versions these days, the internet, to produce an answer. Agentic AI connected to the center repository and other sources, produces answers based on more verifiable, up-to-date sources: the documents in the repository. The result is specific prompt responses that support highly specialized information.
Through applying more sophisticated AI reasoning, agentic AI in contact centers acts as agent-supportive tools. The prompts are supported with real-time information. The response then auto-summarizes the details, and knowledge that surfaces from the prompt responses is applicable without sacrificing quality.
Customer experience leaders relying on the contact center experience thoughtfully reduce cognitive load and have more capacity to focus their skillsets on what matters most. The improved workflow leads to better customer experiences.
The interest in agentic AI reflects the emerging demand for AI agents. Gartner reported a 750% surge in AI-agent-related inquiries between the second and fourth quarters of 2024. The interest was large enough to propel the topic of agentic AI into one of the top strategic tech trends of 2024.
Related Article: What 2025 Data Tells Us About the Future of Chatbots in CX
Generative AI vs Agentic AI in Contact Centers
This table compares the core differences between generative AI and agentic AI in the context of contact center operations, highlighting their strengths and limitations for customer experience leaders.
| Capability | Generative AI | Agentic AI |
|---|---|---|
| Primary Function | Generates responses and content based on training data and, in some cases, internet sources. | Executes tasks autonomously by integrating with real-time systems, repositories and devices. |
| Data Sources | Relies mainly on pre-trained models and static datasets. | Uses live, verifiable data from connected contact center systems and external devices. |
| Interaction Style | Reactive—responds to user prompts with generated output. | Proactive—initiates actions, learns from results and adapts to changing conditions. |
| Accuracy | Dependent on the breadth and quality of training data; may produce “hallucinations.” | Grounded in real-time, domain-specific data, reducing irrelevant or inaccurate outputs. |
| Best Use Cases in Contact Centers | Drafting knowledge base articles, summarizing call transcripts, or answering FAQs. | Intelligent routing, real-time agent coaching, automated follow-ups, and complex task execution. |
Selecting an Agentic AI that Complements Your Contact Center
The high interest in agentic AI means customer experience leaders have choices for incorporating it into a contact center. But how are the best choices determined? There are a number of steps to plan an agentic AI deployment within a contact center.
| Use Case Plan for Contact Center Agentic AI | Description |
|---|---|
| Plan a low-risk pilot | A pilot makes the agentic AI output clear to remove uncertainty in proving how the agentic AI offers value. The pilot is better positioned to deliver tangible initial outcomes before the contact center commits significant investment towards organization-wide rollouts. You should also include business subject-matter experts in the pilot development teams to highlight main concerns early in the workflow plans. |
| Experiment with prebuilt agents and no-code builders | Pilots can experiment with prebuilt agents and no-code builders to see how an integration would work. Customer experience leaders can test agent options, noting how well development and integration tasks are performed while leveraging lower skill and resource barriers. Even the use of a Model Contact Protocol can be considered. |
| Broaden the contact center strategy beyond LLM-based agents into downstream systems | This means evaluating AI agent training platforms that can manage AI agents for more specialized use cases, such as programmatic devices or utilities deployed for product production or service delivery. Pilot teams can also measure how well the system works for other departments and vendors that rely on the contact center activity. Pilot teams can scrutinize vendor concerns and focus on the feasible instances in which AI agents could be most valuable. |
From Pilot to Production: Steps for Implementation
Customers who use digital solutions are seeking speedy delivery. Yet too many tools just add friction. Selecting contact centers with agentic AI systems is the right combination of frictionless, speedy delivery of customer experiences.
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